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上一章案例中的解释变量都是数值，比如匹萨的直接。而很多机器学习问题需要研究的对象可能是分类变量、文字甚至图像。本章，我们介绍提取这些变量特征的方法。这些技术是数据处理的前提——序列化，更是机器学习的基础，影响到本书的所有章节。









分类变量特征提取¶








许多机器学习问题都有分类的、标记的变量，不是连续的。例如，一个应用是用分类特征比如工">
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<article class="post-text h-entry hentry postpage" itemscope="itemscope" itemtype="http://schema.org/Article"><header><h1 class="p-name entry-title" itemprop="headline name"><a href="#" class="u-url">3-feature-extraction-and-preprocessing</a></h1>

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                    Tao Junjie
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            <p class="dateline"><a href="#" rel="bookmark"><time class="published dt-published" datetime="2015-06-24T13:43:42+08:00" itemprop="datePublished" title="2015-06-24 13:43">2015-06-24 13:43</time></a></p>
            
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<h2 id="特征提取与处理">特征提取与处理<a class="anchor-link" href="3-feature-extraction-and-preprocessing.html#%E7%89%B9%E5%BE%81%E6%8F%90%E5%8F%96%E4%B8%8E%E5%A4%84%E7%90%86">¶</a>
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<p>上一章案例中的解释变量都是数值，比如匹萨的直接。而很多机器学习问题需要研究的对象可能是分类变量、文字甚至图像。本章，我们介绍提取这些变量特征的方法。这些技术是数据处理的前提——序列化，更是机器学习的基础，影响到本书的所有章节。</p>
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<h3 id="分类变量特征提取">分类变量特征提取<a class="anchor-link" href="3-feature-extraction-and-preprocessing.html#%E5%88%86%E7%B1%BB%E5%8F%98%E9%87%8F%E7%89%B9%E5%BE%81%E6%8F%90%E5%8F%96">¶</a>
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<p>许多机器学习问题都有分类的、标记的变量，不是连续的。例如，一个应用是用分类特征比如工作地点来预测工资水平。分类变量通常用独热编码（One-of-K or One-Hot Encoding），通过二进制数来表示每个解释变量的特征。</p>
<p>例如，假设<code>city</code>变量有三个值：<code>New York</code>, <code>San Francisco</code>, <code>Chapel Hill</code>。独热编码方式就是用三位二进制数，每一位表示一个城市。</p>
<p>scikit-learn里有<code>DictVectorizer</code>类可以用来表示分类特征：</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.feature_extraction</span> <span class="k">import</span> <span class="n">DictVectorizer</span>
<span class="n">onehot_encoder</span> <span class="o">=</span> <span class="n">DictVectorizer</span><span class="p">()</span>
<span class="n">instances</span> <span class="o">=</span> <span class="p">[{</span><span class="s1">'city'</span><span class="p">:</span> <span class="s1">'New York'</span><span class="p">},{</span><span class="s1">'city'</span><span class="p">:</span> <span class="s1">'San Francisco'</span><span class="p">},</span> <span class="p">{</span><span class="s1">'city'</span><span class="p">:</span> <span class="s1">'Chapel Hill'</span><span class="p">}]</span>
<span class="nb">print</span><span class="p">(</span><span class="n">onehot_encoder</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">instances</span><span class="p">)</span><span class="o">.</span><span class="n">toarray</span><span class="p">())</span>
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<pre>[[ 0.  1.  0.]
 [ 0.  0.  1.]
 [ 1.  0.  0.]]
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<p>会看到，编码的位置并不是与上面城市一一对应的。第一个<code>city</code>编码<code>New York</code>是<code>[ 0.  1.  0.]</code>，用第二个元素为<code>1</code>表示。相比用单独的数值来表示分类，这种方法看起来很直观。<code>New York</code>, <code>San Francisco</code>, <code>Chapel Hill</code>可以表示成<code>1</code>，<code>2</code>，<code>3</code>。数值的大小没有实际意义，城市并没有自然数顺序。</p>

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<h3 id="文字特征提取">文字特征提取<a class="anchor-link" href="3-feature-extraction-and-preprocessing.html#%E6%96%87%E5%AD%97%E7%89%B9%E5%BE%81%E6%8F%90%E5%8F%96">¶</a>
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<p>很多机器学习问题涉及自然语言处理（NLP），必然要处理文字信息。文字必须转换成可以量化的特征向量。下面我们就来介绍最常用的文字表示方法：词库模型（Bag-of-words model）。</p>

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<h4 id="词库表示法">词库表示法<a class="anchor-link" href="3-feature-extraction-and-preprocessing.html#%E8%AF%8D%E5%BA%93%E8%A1%A8%E7%A4%BA%E6%B3%95">¶</a>
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<p>词库模型是文字模型化的最常用方法。对于一个文档（document），忽略其词序和语法，句法，将其仅仅看做是一个词集合，或者说是词的一个组合，文档中每个词的出现都是独立的，不依赖于其他词是否出现，或者说当这篇文章的作者在任意一个位置选择一个词汇都不受前面句子的影响而独立选择的。词库模型可以看成是独热编码的一种扩展，它为每个单词设值一个特征值。词库模型依据是用类似单词的文章意思也差不多。词库模型可以通过有限的编码信息实现有效的文档分类和检索。</p>
<p>一批文档的集合称为文集（corpus）。让我们用一个由两个文档组成的文集来演示词库模型：</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">corpus</span> <span class="o">=</span> <span class="p">[</span>
    <span class="s1">'UNC played Duke in basketball'</span><span class="p">,</span>
    <span class="s1">'Duke lost the basketball game'</span>
<span class="p">]</span>
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<p>文集包括8个词：<code>UNC</code>, <code>played</code>, <code>Duke</code>, <code>in</code>, <code>basketball</code>, <code>lost</code>, <code>the</code>, <code>game</code>。文件的单词构成词汇表（vocabulary）。词库模型用文集的词汇表中每个单词的特征向量表示每个文档。我们的文集有8个单词，那么每个文档就是由一个包含8位元素的向量构成。构成特征向量的元素数量称为维度（dimension）。用一个词典（dictionary）来表示词汇表与特征向量索引的对应关系。</p>
<p>在大多数词库模型中，特征向量的每一个元素是用二进制数表示单词是否在文档中。例如，第一个文档的第一个词是<code>UNC</code>，词汇表的第一个单词是<code>UNC</code>，因此特征向量的第一个元素就是<code>1</code>。词汇表的最后一个单词是<code>game</code>。第一个文档没有这个词，那么特征向量的最后一个元素就是<code>0</code>。<code>CountVectorizer</code>类会把文档全部转换成小写，然后将文档词块化（tokenize）。文档词块化是把句子分割成词块（token）或有意义的字母序列的过程。词块大多是单词，但是他们也可能是一些短语，如标点符号和词缀。<code>CountVectorizer</code>类通过正则表达式用空格分割句子，然后抽取长度大于等于2的字母序列。scikit-learn实现代码如下：</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.feature_extraction.text</span> <span class="k">import</span> <span class="n">CountVectorizer</span>
<span class="n">corpus</span> <span class="o">=</span> <span class="p">[</span>
    <span class="s1">'UNC played Duke in basketball'</span><span class="p">,</span>
    <span class="s1">'Duke lost the basketball game'</span>
<span class="p">]</span>
<span class="n">vectorizer</span> <span class="o">=</span> <span class="n">CountVectorizer</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="n">vectorizer</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">corpus</span><span class="p">)</span><span class="o">.</span><span class="n">todense</span><span class="p">())</span>
<span class="nb">print</span><span class="p">(</span><span class="n">vectorizer</span><span class="o">.</span><span class="n">vocabulary_</span><span class="p">)</span>
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<pre>[[1 1 0 1 0 1 0 1]
 [1 1 1 0 1 0 1 0]]
{'unc': 7, 'played': 5, 'game': 2, 'in': 3, 'basketball': 0, 'the': 6, 'duke': 1, 'lost': 4}
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<p>让我们再增加一个文档到文集里：</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">corpus</span> <span class="o">=</span> <span class="p">[</span>
    <span class="s1">'UNC played Duke in basketball'</span><span class="p">,</span>
    <span class="s1">'Duke lost the basketball game'</span><span class="p">,</span>
    <span class="s1">'I ate a sandwich'</span>
<span class="p">]</span>
<span class="n">vectorizer</span> <span class="o">=</span> <span class="n">CountVectorizer</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="n">vectorizer</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">corpus</span><span class="p">)</span><span class="o">.</span><span class="n">todense</span><span class="p">())</span>
<span class="nb">print</span><span class="p">(</span><span class="n">vectorizer</span><span class="o">.</span><span class="n">vocabulary_</span><span class="p">)</span>
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<pre>[[0 1 1 0 1 0 1 0 0 1]
 [0 1 1 1 0 1 0 0 1 0]
 [1 0 0 0 0 0 0 1 0 0]]
{'unc': 9, 'played': 6, 'game': 3, 'in': 4, 'ate': 0, 'basketball': 1, 'the': 8, 'sandwich': 7, 'duke': 2, 'lost': 5}
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<p>通过<code>CountVectorizer</code>类可以得出上面的结果。词汇表里面有10个单词，但<code>a</code>不在词汇表里面，是因为<code>a</code>的长度不符合<code>CountVectorizer</code>类的要求。</p>
<p>对比文档的特征向量，会发现前两个文档相比第三个文档更相似。如果用欧氏距离（Euclidean distance）计算它们的特征向量会比其与第三个文档距离更接近。两向量的欧氏距离就是两个向量欧氏范数（Euclidean norm）或L2范数差的绝对值：</p>
$$
d = \begin{Vmatrix}
x_0 - x_1 \\
\end{Vmatrix}
$$<p>向量的欧氏范数是其元素平方和的平方根：</p>
$$
\begin{Vmatrix}
x \\
\end{Vmatrix}
=\sqrt{x_1^2 + x_2^2 + \cdots + x_n^2}
$$<p>scikit-learn里面的<code>euclidean_distances</code>函数可以计算若干向量的距离，表示两个语义最相似的文档其向量在空间中也是最接近的。</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.metrics.pairwise</span> <span class="k">import</span> <span class="n">euclidean_distances</span>
<span class="n">counts</span> <span class="o">=</span> <span class="n">vectorizer</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">corpus</span><span class="p">)</span><span class="o">.</span><span class="n">todense</span><span class="p">()</span>
<span class="k">for</span> <span class="n">x</span><span class="p">,</span><span class="n">y</span> <span class="ow">in</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">],[</span><span class="mi">0</span><span class="p">,</span><span class="mi">2</span><span class="p">],[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">]]:</span>
    <span class="n">dist</span> <span class="o">=</span> <span class="n">euclidean_distances</span><span class="p">(</span><span class="n">counts</span><span class="p">[</span><span class="n">x</span><span class="p">],</span><span class="n">counts</span><span class="p">[</span><span class="n">y</span><span class="p">])</span>
    <span class="nb">print</span><span class="p">(</span><span class="s1">'文档</span><span class="si">{}</span><span class="s1">与文档</span><span class="si">{}</span><span class="s1">的距离</span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">,</span><span class="n">dist</span><span class="p">))</span>
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<pre>文档0与文档1的距离[[ 2.44948974]]
文档0与文档2的距离[[ 2.64575131]]
文档1与文档2的距离[[ 2.64575131]]
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<p>如果我们用新闻报道内容做文集，词汇表就可以用成千上万个单词。每篇新闻的特征向量都会有成千上万个元素，很多元素都会是0。体育新闻不会包含财经新闻的术语，同样文化新闻也不会包含财经新闻的术语。有许多零元素的高维特征向量成为稀疏向量（sparse vectors）。</p>
<p>用高维数据可以量化机器学习任务时会有一些问题，不只是出现在自然语言处理领域。第一个问题就是高维向量需要占用更大内存。NumPy提供了一些数据类型只显示稀疏向量的非零元素，可以有效处理这个问题。</p>
<p>第二个问题就是著名的维度灾难（curse of dimensionality，Hughes effect），维度越多就要求更大的训练集数据保证模型能够充分学习。如果训练样本不够，那么算法就可以拟合过度导致归纳失败。下面，我们介绍一些降维的方法。在第7章，PCA降维里面，我们还会介绍用数值方法降维。</p>

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<h4 id="停用词过滤">停用词过滤<a class="anchor-link" href="3-feature-extraction-and-preprocessing.html#%E5%81%9C%E7%94%A8%E8%AF%8D%E8%BF%87%E6%BB%A4">¶</a>
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<p>特征向量降维的一个基本方法是单词全部转换成小写。这是因为单词的大小写一般不会影响意思。而首字母大写的单词一般只是在句子的开头，而词库模型并不在乎单词的位置和语法。</p>
<p>另一种方法是去掉文集常用词。这里词称为停用词（Stop-word），像<code>a</code>，<code>an</code>，<code>the</code>，助动词<code>do</code>，<code>be</code>，<code>will</code>，介词<code>on</code>，<code>around</code>，<code>beneath</code>等。停用词通常是构建文档意思的功能词汇，其字面意义并不体现。<code>CountVectorizer</code>类可以通过设置<code>stop_words</code>参数过滤停用词，默认是英语常用的停用词。</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">corpus</span> <span class="o">=</span> <span class="p">[</span>
    <span class="s1">'UNC played Duke in basketball'</span><span class="p">,</span>
    <span class="s1">'Duke lost the basketball game'</span><span class="p">,</span>
    <span class="s1">'I ate a sandwich'</span>
<span class="p">]</span>
<span class="n">vectorizer</span> <span class="o">=</span> <span class="n">CountVectorizer</span><span class="p">(</span><span class="n">stop_words</span><span class="o">=</span><span class="s1">'english'</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">vectorizer</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">corpus</span><span class="p">)</span><span class="o">.</span><span class="n">todense</span><span class="p">())</span>
<span class="nb">print</span><span class="p">(</span><span class="n">vectorizer</span><span class="o">.</span><span class="n">vocabulary_</span><span class="p">)</span>
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<pre>[[0 1 1 0 0 1 0 1]
 [0 1 1 1 1 0 0 0]
 [1 0 0 0 0 0 1 0]]
{'unc': 7, 'played': 5, 'game': 3, 'ate': 0, 'basketball': 1, 'sandwich': 6, 'duke': 2, 'lost': 4}
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<p>这样停用词就没有了，前两篇文档依然相比其与第三篇的内容更接近。</p>

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<h4 id="词根还原与词形还原">词根还原与词形还原<a class="anchor-link" href="3-feature-extraction-and-preprocessing.html#%E8%AF%8D%E6%A0%B9%E8%BF%98%E5%8E%9F%E4%B8%8E%E8%AF%8D%E5%BD%A2%E8%BF%98%E5%8E%9F">¶</a>
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<p>停用词去掉之后，可能还会剩下许多词，还有一种常用的方法就是词根还原（stemming ）与词形还原（lemmatization）。</p>
<p>特征向量里面的单词很多都是一个词的不同形式，比如<code>jumping</code>和<code>jumps</code>都是<code>jump</code>的不同形式。词根还原与词形还原就是为了将单词从不同的时态、派生形式还原。</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.feature_extraction.text</span> <span class="k">import</span> <span class="n">CountVectorizer</span>
<span class="n">corpus</span> <span class="o">=</span> <span class="p">[</span>
    <span class="s1">'He ate the sandwiches'</span><span class="p">,</span>
    <span class="s1">'Every sandwich was eaten by him'</span>
<span class="p">]</span>
<span class="n">vectorizer</span> <span class="o">=</span> <span class="n">CountVectorizer</span><span class="p">(</span><span class="n">binary</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">stop_words</span><span class="o">=</span><span class="s1">'english'</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">vectorizer</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">corpus</span><span class="p">)</span><span class="o">.</span><span class="n">todense</span><span class="p">())</span>
<span class="nb">print</span><span class="p">(</span><span class="n">vectorizer</span><span class="o">.</span><span class="n">vocabulary_</span><span class="p">)</span>
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<pre>[[1 0 0 1]
 [0 1 1 0]]
{'sandwich': 2, 'sandwiches': 3, 'ate': 0, 'eaten': 1}
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<p>这两个文档意思差不多，但是其特征向量完全不同，因为单词的形式不同。两个单词都是有一个动词<code>eat</code>和一个<code>sandwich</code>，这些特征应该在向量中反映出来。词形还原就是用来处理可以表现单词意思的词元（lemma）或形态学的词根（morphological root）的过程。词元是单词在词典中查询该词的基本形式。词根还原与词形还原类似，但它不是生成单词的形态学的词根。而是把附加的词缀都去掉，构成一个词块，可能不是一个正常的单词。词形还原通常需要词法资料的支持，比如WordNet和单词词类（part of speech）。词根还原算法通常需要用规则产生词干（stem）并操作词块，不需要词法资源，也不在乎单词的意思。</p>
<p>让我们分析一下单词<code>gathering</code>的词形还原：</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">corpus</span> <span class="o">=</span> <span class="p">[</span>
    <span class="s1">'I am gathering ingredients for the sandwich.'</span><span class="p">,</span>
    <span class="s1">'There were many wizards at the gathering.'</span>
<span class="p">]</span>
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<p>第一句的<code>gathering</code>是动词，其词元是<code>gather</code>。后一句的<code>gathering</code>是名词，其词元是<code>gathering</code>。我们用Python的<a href="http://www.nltk.org/install.html">NLTK（Natural
Language Tool Kit）</a>库来处理。</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">nltk</span>
<span class="n">nltk</span><span class="o">.</span><span class="n">download</span><span class="p">()</span>
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<pre>showing info http://www.nltk.org/nltk_data/
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<pre>True</pre>
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<p>NLTK的<code>WordNetLemmatizer</code>可以用<code>gathering</code>的词类确定词元。</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">nltk.stem.wordnet</span> <span class="k">import</span> <span class="n">WordNetLemmatizer</span>
<span class="n">lemmatizer</span> <span class="o">=</span> <span class="n">WordNetLemmatizer</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="n">lemmatizer</span><span class="o">.</span><span class="n">lemmatize</span><span class="p">(</span><span class="s1">'gathering'</span><span class="p">,</span> <span class="s1">'v'</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">lemmatizer</span><span class="o">.</span><span class="n">lemmatize</span><span class="p">(</span><span class="s1">'gathering'</span><span class="p">,</span> <span class="s1">'n'</span><span class="p">))</span>
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<pre>gather
gathering
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<p>我们把词形还原应用到之前的例子里：</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">nltk</span> <span class="k">import</span> <span class="n">word_tokenize</span>
<span class="kn">from</span> <span class="nn">nltk.stem</span> <span class="k">import</span> <span class="n">PorterStemmer</span>
<span class="kn">from</span> <span class="nn">nltk.stem.wordnet</span> <span class="k">import</span> <span class="n">WordNetLemmatizer</span>
<span class="kn">from</span> <span class="nn">nltk</span> <span class="k">import</span> <span class="n">pos_tag</span>
<span class="n">wordnet_tags</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'n'</span><span class="p">,</span> <span class="s1">'v'</span><span class="p">]</span>
<span class="n">corpus</span> <span class="o">=</span> <span class="p">[</span>
    <span class="s1">'He ate the sandwiches'</span><span class="p">,</span>
    <span class="s1">'Every sandwich was eaten by him'</span>
<span class="p">]</span>

<span class="n">stemmer</span> <span class="o">=</span> <span class="n">PorterStemmer</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Stemmed:'</span><span class="p">,</span> <span class="p">[[</span><span class="n">stemmer</span><span class="o">.</span><span class="n">stem</span><span class="p">(</span><span class="n">token</span><span class="p">)</span> <span class="k">for</span> <span class="n">token</span> <span class="ow">in</span> <span class="n">word_tokenize</span><span class="p">(</span><span class="n">document</span><span class="p">)]</span> <span class="k">for</span> <span class="n">document</span> <span class="ow">in</span> <span class="n">corpus</span><span class="p">])</span>
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<pre>Stemmed: [['He', 'ate', 'the', 'sandwich'], ['Everi', 'sandwich', 'wa', 'eaten', 'by', 'him']]
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">lemmatize</span><span class="p">(</span><span class="n">token</span><span class="p">,</span> <span class="n">tag</span><span class="p">):</span>
    <span class="k">if</span> <span class="n">tag</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">'n'</span><span class="p">,</span> <span class="s1">'v'</span><span class="p">]:</span>
        <span class="k">return</span> <span class="n">lemmatizer</span><span class="o">.</span><span class="n">lemmatize</span><span class="p">(</span><span class="n">token</span><span class="p">,</span> <span class="n">tag</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">lower</span><span class="p">())</span>
    <span class="k">return</span> <span class="n">token</span>

<span class="n">lemmatizer</span> <span class="o">=</span> <span class="n">WordNetLemmatizer</span><span class="p">()</span>
<span class="n">tagged_corpus</span> <span class="o">=</span> <span class="p">[</span><span class="n">pos_tag</span><span class="p">(</span><span class="n">word_tokenize</span><span class="p">(</span><span class="n">document</span><span class="p">))</span> <span class="k">for</span> <span class="n">document</span> <span class="ow">in</span> <span class="n">corpus</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Lemmatized:'</span><span class="p">,</span> <span class="p">[[</span><span class="n">lemmatize</span><span class="p">(</span><span class="n">token</span><span class="p">,</span> <span class="n">tag</span><span class="p">)</span> <span class="k">for</span> <span class="n">token</span><span class="p">,</span> <span class="n">tag</span> <span class="ow">in</span> <span class="n">document</span><span class="p">]</span> <span class="k">for</span> <span class="n">document</span> <span class="ow">in</span> <span class="n">tagged_corpus</span><span class="p">])</span>
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<pre>Lemmatized: [['He', 'eat', 'the', 'sandwich'], ['Every', 'sandwich', 'be', 'eat', 'by', 'him']]
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<p>通过词根还原与词形还原可以有效降维，去掉不必要的单词形式，特征向量可以更有效的表示文档的意思。</p>

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<h4 id="带TF-IDF权重的扩展词库">带TF-IDF权重的扩展词库<a class="anchor-link" href="3-feature-extraction-and-preprocessing.html#%E5%B8%A6TF-IDF%E6%9D%83%E9%87%8D%E7%9A%84%E6%89%A9%E5%B1%95%E8%AF%8D%E5%BA%93">¶</a>
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<p>前面我们用词库模型构建了判断单词是个在文档中出现的特征向量。这些特征向量与单词的语法，顺序，频率无关。不过直觉告诉我们文档中单词的频率对文档的意思有重要作用。一个文档中某个词多次出现，相比只出现过一次的单词更能体现反映文档的意思。现在我们就将单词频率加入特征向量，然后介绍由词频引出的两个问题。</p>
<p>我们用一个整数来代码单词的频率。代码如下：</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.feature_extraction.text</span> <span class="k">import</span> <span class="n">CountVectorizer</span>
<span class="n">corpus</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'The dog ate a sandwich, the wizard transfigured a sandwich, and I ate a sandwich'</span><span class="p">]</span>
<span class="n">vectorizer</span> <span class="o">=</span> <span class="n">CountVectorizer</span><span class="p">(</span><span class="n">stop_words</span><span class="o">=</span><span class="s1">'english'</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">vectorizer</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">corpus</span><span class="p">)</span><span class="o">.</span><span class="n">todense</span><span class="p">())</span>
<span class="nb">print</span><span class="p">(</span><span class="n">vectorizer</span><span class="o">.</span><span class="n">vocabulary_</span><span class="p">)</span>
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<pre>[[2 1 3 1 1]]
{'wizard': 4, 'transfigured': 3, 'sandwich': 2, 'dog': 1, 'ate': 0}
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<p>结果中第一行是单词的频率，<code>dog</code>频率为<code>1</code>，<code>sandwich</code>频率为<code>3</code>。注意和前面不同的是，<code>binary=True</code>没有了，因为<code>binary</code>默认是<code>False</code>，这样返回的是词汇表的词频，不是二进制结果<code>[1 1 1 1 1]</code>。这种单词频率构成的特征向量为文档的意思提供了更多的信息，但是在对比不同的文档时，需要考虑文档的长度。</p>
<p>很多单词可能在两个文档的频率一样，但是两个文档的长度差别很大，一个文档比另一个文档长很多倍。scikit-learn的<code>TfdfTransformer</code>类可以解决这个问题，通过对词频（term frequency）特征向量归一化来实现不同文档向量的可比性。默认情况下，<code>TfdfTransformer</code>类用L2范数对特征向量归一化：</p>
$$tf(t,d)= \frac {f(t,d)+1} {\begin{Vmatrix}x\end{Vmatrix}}$$<p>$f(t,d)$是第$d$个文档（document）第$t$个单词（term）的频率，$\begin{Vmatrix}x\end{Vmatrix}$是频率向量的L2范数。另外，还有对数词频调整方法（logarithmically scaled term frequencies），把词频调整到一个更小的范围，或者词频放大法（augmented term frequencies），适用于消除较长文档的差异。对数词频公式如下：</p>
$$tf(t,d)= log(f(t,d)+1)$$<p><code>TfdfTransformer</code>类计算对数词频调整时，需要将参数<code>sublinear_tf</code>设置为<code>True</code>。词频放大公式如下：</p>
$$tf(t,d)= 0.5 + \frac {f(t,d)+1} {max f(w,d): w \in d}$$<p>$max f(w,d): w \in d$是文档$d$中的最大词频。scikit-learn没有现成可用的词频放大公式，不过通过<code>CountVectorizer</code>可以轻松实现。</p>
<p>归一化，对数调整词频和词频放大三支方法都消除文档不同大小对词频的影响。但是，另一个问题仍然存在，那就是特征向量里高频词的权重更大，即使这些词在文集内其他文档里面也经常出现。这些单词并没有突出代表单个文档的意思。比如，一个文集里大多数文档都是关于杜克大学篮球队的，那么高频词就是<code>basketball</code>，<code>Coach K</code>，<code>flop</code>。这些词可以被看成是该文集的停用词，因为它们太普遍对区分文档的意思没任何作用。逆向文件频率（inverse document frequency，IDF）就是用来度量文集中单词频率的。</p>
$$idf(t,D)= log(\frac N {1+|d \in D:t \in d|})$$<p>其中，$N$是文集中文档数量，$d \in D:t \in d$表示包含单词$t$的文档数量。单词的TF-IDF值就是其频率与逆向文件频率的乘积。</p>
<p><code>TfdfTransformer</code>类默认返回TF-IDF值，其参数<code>use_idf</code>默认为<code>True</code>。由于TF-IDF加权特征向量经常用来表示文本，所以scikit-learn提供了<code>TfidfVectorizer</code>类将<code>CountVectorizer</code>和<code>TfdfTransformer</code>类封装在一起。</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.feature_extraction.text</span> <span class="k">import</span> <span class="n">TfidfVectorizer</span>
<span class="n">corpus</span> <span class="o">=</span> <span class="p">[</span>
    <span class="s1">'The dog ate a sandwich and I ate a sandwich'</span><span class="p">,</span>
    <span class="s1">'The wizard transfigured a sandwich'</span>
<span class="p">]</span>
<span class="n">vectorizer</span> <span class="o">=</span> <span class="n">TfidfVectorizer</span><span class="p">(</span><span class="n">stop_words</span><span class="o">=</span><span class="s1">'english'</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">vectorizer</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">corpus</span><span class="p">)</span><span class="o">.</span><span class="n">todense</span><span class="p">())</span>
<span class="nb">print</span><span class="p">(</span><span class="n">vectorizer</span><span class="o">.</span><span class="n">vocabulary_</span><span class="p">)</span>
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<pre>[[ 0.75458397  0.37729199  0.53689271  0.          0.        ]
 [ 0.          0.          0.44943642  0.6316672   0.6316672 ]]
{'wizard': 4, 'transfigured': 3, 'sandwich': 2, 'dog': 1, 'ate': 0}
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<p>通过TF-IDF加权之后，我们会发现在文集中较常见的词，如<code>sandwich</code>被调整了。</p>

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<h4 id="通过哈希技巧实现特征向量">通过哈希技巧实现特征向量<a class="anchor-link" href="3-feature-extraction-and-preprocessing.html#%E9%80%9A%E8%BF%87%E5%93%88%E5%B8%8C%E6%8A%80%E5%B7%A7%E5%AE%9E%E7%8E%B0%E7%89%B9%E5%BE%81%E5%90%91%E9%87%8F">¶</a>
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<p>前面我们是用包含文集所有词块的词典来完成文档词块与特征向量的映射的。这么做有两个缺点。首先是文集需要被调用两次。第一次是创建词典，第二次是创建文档的特征向量。另外，词典必须储存在内存里，如果文集特别大就会很耗内存。通过哈希表可以有效的解决这些问题。可以将词块用哈希函数来确定它在特征向量的索引位置，可以不创建词典，这称为哈希技巧（hashing trick）。scikit-learn提供了<code>HashingVectorizer</code>来实现这个技巧：</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.feature_extraction.text</span> <span class="k">import</span> <span class="n">HashingVectorizer</span>
<span class="n">corpus</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'the'</span><span class="p">,</span> <span class="s1">'ate'</span><span class="p">,</span> <span class="s1">'bacon'</span><span class="p">,</span> <span class="s1">'cat'</span><span class="p">]</span>
<span class="n">vectorizer</span> <span class="o">=</span> <span class="n">HashingVectorizer</span><span class="p">(</span><span class="n">n_features</span><span class="o">=</span><span class="mi">6</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">vectorizer</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">corpus</span><span class="p">)</span><span class="o">.</span><span class="n">todense</span><span class="p">())</span>
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<pre>[[-1.  0.  0.  0.  0.  0.]
 [ 0.  0.  0.  1.  0.  0.]
 [ 0.  0.  0.  0. -1.  0.]
 [ 0.  1.  0.  0.  0.  0.]]
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<p>哈希技巧是无固定状态的（stateless），，它把任意的数据块映射到固定数目的位置，并且保证相同的输入一定产生相同的输出，不同的输入尽可能产生不同的输出。它可以用并行，线上，流式传输创建特征向量，因为它初始化是不需要文集输入。<code>n_features</code>是一个可选参数，默认值是$2^{20}$，这里设置成<code>6</code>是为了演示。另外，注意有些单词频率是负数。由于Hash碰撞可能发生，所以<code>HashingVectorizer</code>用有符号哈希函数（signed hash function）。特征值和它的词块的哈希值带同样符号，如果<code>cats</code>出现过两次，被哈希成<code>-3</code>，文档特征向量的第四个元素要减去<code>2</code>。如果<code>dogs</code>出现过两次，被哈希成<code>3</code>，文档特征向量的第四个元素要加上<code>2</code>。</p>
<p>用带符号哈希函数可以把词块发生哈希碰撞的概率相互抵消掉，信息损失比信息损失的同时出现信息冗余要好。哈希技巧的一个不足是模型的结果更难察看，由于哈希函数不能显示哪个词块映射到特征向量的哪个位置了。</p>

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<h3 id="图片特征提取">图片特征提取<a class="anchor-link" href="3-feature-extraction-and-preprocessing.html#%E5%9B%BE%E7%89%87%E7%89%B9%E5%BE%81%E6%8F%90%E5%8F%96">¶</a>
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<p>计算机视觉是一门研究如何使机器“看”的科学，让计算机学会处理和理解图像。这门学问有时需要借助机器学习。本章介绍一些机器学习在计算机视觉领域应用的基础技术。</p>

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<h4 id="通过像素值提取特征">通过像素值提取特征<a class="anchor-link" href="3-feature-extraction-and-preprocessing.html#%E9%80%9A%E8%BF%87%E5%83%8F%E7%B4%A0%E5%80%BC%E6%8F%90%E5%8F%96%E7%89%B9%E5%BE%81">¶</a>
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<p>数字图像通常是一张光栅图或像素图，将颜色映射到网格坐标里。一张图片可以看成是一个每个元素都是颜色值的矩阵。表示图像基本特征就是将矩阵每行连起来变成一个行向量。光学文字识别（Optical character recognition，OCR）是机器学习的经典问题。下面我们用这个技术来识别手写数字。</p>
<p>scikit-learn的<code>digits</code>数字集包括至少1700种0-9的手写数字图像。每个图像都有8x8像像素构成。每个像素的值是0-16，白色是0，黑色是16。如下图所示：</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="k">import</span> <span class="n">datasets</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="n">digits</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">load_digits</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Digit:'</span><span class="p">,</span> <span class="n">digits</span><span class="o">.</span><span class="n">target</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="n">digits</span><span class="o">.</span><span class="n">images</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">'off'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">digits</span><span class="o">.</span><span class="n">images</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">gray_r</span><span class="p">,</span> <span class="n">interpolation</span><span class="o">=</span><span class="s1">'nearest'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<pre>Digit: 0
[[  0.   0.   5.  13.   9.   1.   0.   0.]
 [  0.   0.  13.  15.  10.  15.   5.   0.]
 [  0.   3.  15.   2.   0.  11.   8.   0.]
 [  0.   4.  12.   0.   0.   8.   8.   0.]
 [  0.   5.   8.   0.   0.   9.   8.   0.]
 [  0.   4.  11.   0.   1.  12.   7.   0.]
 [  0.   2.  14.   5.  10.  12.   0.   0.]
 [  0.   0.   6.  13.  10.   0.   0.   0.]]
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<p>我们将8x8矩阵转换成64维向量来创建一个特征向量：</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">digits</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">load_digits</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Feature vector:</span><span class="se">\n</span><span class="s1">'</span><span class="p">,</span> <span class="n">digits</span><span class="o">.</span><span class="n">images</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">64</span><span class="p">))</span>
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<pre>Feature vector:
 [[  0.   0.   5.  13.   9.   1.   0.   0.   0.   0.  13.  15.  10.  15.
    5.   0.   0.   3.  15.   2.   0.  11.   8.   0.   0.   4.  12.   0.
    0.   8.   8.   0.   0.   5.   8.   0.   0.   9.   8.   0.   0.   4.
   11.   0.   1.  12.   7.   0.   0.   2.  14.   5.  10.  12.   0.   0.
    0.   0.   6.  13.  10.   0.   0.   0.]]
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<p>这样表示可以有效的处理一些基本任务，比如识别手写字母等。但是，记录每个像素的数值在大图像处理时不太好用。一个100x100像素的图像其灰度图产生的特征向量是10000维度，而1920x1080像素的图像是2073600。和TF-IDF特征向量不同，大部分图像都不是稀疏的。这种表示法的缺点不只是特征向量的维度灾难，还有就是某个位置的学习结果在经过对图像的放缩，旋转或变换之后可能就不对了，非常敏感，缺乏稳定性。另外，这种方法对图像的亮度也十分敏感。所以这种方法在处理照片和其他自然景色图像时不怎么有用。现代计算机视觉应用通常手工实现特征提取，或者用深度学习自动化解决无监督问题。后面我们会详细介绍。</p>

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<h4 id="对感兴趣的点进行特征提取">对感兴趣的点进行特征提取<a class="anchor-link" href="3-feature-extraction-and-preprocessing.html#%E5%AF%B9%E6%84%9F%E5%85%B4%E8%B6%A3%E7%9A%84%E7%82%B9%E8%BF%9B%E8%A1%8C%E7%89%B9%E5%BE%81%E6%8F%90%E5%8F%96">¶</a>
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<p>前面创建的特征矢量包含了图像的每个像素，既包含了图像特征的有用信息，也包含了一堆噪声。查看图像后，我们会发现所有的图像都有一个白边，这些像素是没用的。人们不需要观察物体的每个属性就可以很快的识别出很多物体。我们可以根据轮廓识别出汽车，并不需要观察后视镜，我们也可以通过一个鼻子或嘴巴判断图像是一个人。这些直觉就可以用来建立一种表示图像大多数信息属性的方法。这些有信息量的属性，称为兴趣点（points of interest），是由丰富的纹理包围，基本可以重建图像。边缘（edges）和角点（corners）是两种常用的兴趣点类型。边是像素快速变化的分界线（boundary），角是两条边的交集。我们用scikit-image库抽取下图的兴趣点：</p>
<p><img src="mlslpic/3.1%20mandrill.png" alt="mandrill"></p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">skimage.feature</span> <span class="k">import</span> <span class="n">corner_harris</span><span class="p">,</span> <span class="n">corner_peaks</span>
<span class="kn">from</span> <span class="nn">skimage.color</span> <span class="k">import</span> <span class="n">rgb2gray</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">skimage.io</span> <span class="k">as</span> <span class="nn">io</span>
<span class="kn">from</span> <span class="nn">skimage.exposure</span> <span class="k">import</span> <span class="n">equalize_hist</span>


<span class="k">def</span> <span class="nf">show_corners</span><span class="p">(</span><span class="n">corners</span><span class="p">,</span> <span class="n">image</span><span class="p">):</span>
    <span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">gray</span><span class="p">()</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">image</span><span class="p">)</span>
    <span class="n">y_corner</span><span class="p">,</span> <span class="n">x_corner</span> <span class="o">=</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">corners</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x_corner</span><span class="p">,</span> <span class="n">y_corner</span><span class="p">,</span> <span class="s1">'or'</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">image</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="n">image</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">0</span><span class="p">)</span>
    <span class="n">fig</span><span class="o">.</span><span class="n">set_size_inches</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">fig</span><span class="o">.</span><span class="n">get_size_inches</span><span class="p">())</span> <span class="o">*</span> <span class="mf">1.5</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>

<span class="n">mandrill</span> <span class="o">=</span> <span class="n">io</span><span class="o">.</span><span class="n">imread</span><span class="p">(</span><span class="s1">'mlslpic/3.1 mandrill.png'</span><span class="p">)</span>
<span class="n">mandrill</span> <span class="o">=</span> <span class="n">equalize_hist</span><span class="p">(</span><span class="n">rgb2gray</span><span class="p">(</span><span class="n">mandrill</span><span class="p">))</span>
<span class="n">corners</span> <span class="o">=</span> <span class="n">corner_peaks</span><span class="p">(</span><span class="n">corner_harris</span><span class="p">(</span><span class="n">mandrill</span><span class="p">),</span> <span class="n">min_distance</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="n">show_corners</span><span class="p">(</span><span class="n">corners</span><span class="p">,</span> <span class="n">mandrill</span><span class="p">)</span>
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TycSVN9/kzXmH4C/dbu4+f57Njz3G+Pg4Y729Cz5TTKkkLS2N0a4ukufOIY9G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">
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<p>上图就是兴趣点的提取结果。图片的230400个像素中，466个兴趣点被提取。这种提取方式更紧凑，而且当图片的亮度发生统一变化时，这些兴趣点依然存在。</p>

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<h4 id="SIFT和SURF">SIFT和SURF<a class="anchor-link" href="3-feature-extraction-and-preprocessing.html#SIFT%E5%92%8CSURF">¶</a>
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<p>尺度不变特征转换（Scale-Invariant Feature Transform，SIFT）是一种特征提取方法，相比前面使用的方法，SIFT对图像的尺寸，旋转，亮度变化更不敏感。每个SIFT特征都是一个描述图片上某个区域边缘和角点的向量。和兴趣点不同，SIFT还可以获取每个兴趣点和它周围点的综合信息。加速稳健特征（Speeded-Up Robust Features，SURF）是另一个抽取图像兴趣点的方法，其特征向量对图像的尺寸，旋转，亮度变化是不变的。SURF的算法可以比SIFT更快，更有效的识别出兴趣点。</p>
<p>两种方法的具体理论解释在数字图像处理类的教材中都有介绍，感兴趣的同学可以研究。这样用<code>mahotas</code>库来应用SURF方法处理下面的图片。</p>
<p><img src="mlslpic/3.2%20xiaobao.png" alt="xiaobao"></p>
<p>和兴趣点抽取类似，抽取SURF只是机器学习中创建特征向量的第一步。训练集的每个实例都会抽取不同的SURF。第六章的，K-Means聚类，我们会介绍聚类方法抽取SURF来学习特征，可以作为一种图像分类方法。<code>mahotas</code>代码如下：</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">mahotas</span> <span class="k">as</span> <span class="nn">mh</span>
<span class="kn">from</span> <span class="nn">mahotas.features</span> <span class="k">import</span> <span class="n">surf</span>

<span class="n">image</span> <span class="o">=</span> <span class="n">mh</span><span class="o">.</span><span class="n">imread</span><span class="p">(</span><span class="s1">'mlslpic/3.2 xiaobao.png'</span><span class="p">,</span> <span class="n">as_grey</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'第一个SURF描述符：</span><span class="se">\n</span><span class="si">{}</span><span class="se">\n</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">surf</span><span class="o">.</span><span class="n">surf</span><span class="p">(</span><span class="n">image</span><span class="p">)[</span><span class="mi">0</span><span class="p">]))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'抽取了</span><span class="si">%s</span><span class="s1">个SURF描述符'</span> <span class="o">%</span> <span class="nb">len</span><span class="p">(</span><span class="n">surf</span><span class="o">.</span><span class="n">surf</span><span class="p">(</span><span class="n">image</span><span class="p">)))</span>
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<pre>第一个SURF描述符：
[  1.15299134e+02   2.56185453e+02   3.51230841e+00   3.32786485e+02
   1.00000000e+00   1.75644866e+00  -2.94268692e-03   3.30736379e-03
   2.94268692e-03   3.30736379e-03  -2.58778609e-02   3.25587066e-02
   2.58778609e-02   3.25587066e-02  -3.03768176e-02   4.18212640e-02
   3.03768176e-02   4.18212640e-02  -5.75169209e-03   7.66422266e-03
   5.75169209e-03   7.66422266e-03  -1.85200481e-02   3.10523761e-02
   1.85200481e-02   3.10523761e-02  -9.61023554e-02   2.59842816e-01
   1.12794174e-01   2.59842816e-01  -6.66368114e-02   2.72006376e-01
   1.40583321e-01   2.72006376e-01  -1.91014197e-02   5.28250599e-02
   2.03376276e-02   5.28250599e-02  -2.24247135e-02   3.35105185e-02
   2.24247135e-02   3.35105185e-02  -2.36547964e-01   3.18867366e-01
   2.36547964e-01   3.18867366e-01  -2.49737941e-01   3.00644512e-01
   2.50125503e-01   3.03596724e-01  -1.69936886e-02   3.82398567e-02
   2.00617910e-02   3.82398567e-02  -3.72417955e-03   5.53246035e-03
   3.72417955e-03   5.53246035e-03  -2.99748321e-02   4.76884368e-02
   2.99748321e-02   4.76884368e-02  -5.12157923e-02   7.42619311e-02
   5.12284796e-02   7.42619311e-02  -1.02035696e-02   1.19729640e-02
   1.02035696e-02   1.19729640e-02]

抽取了588个SURF描述符
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<h3 id="数据标准化">数据标准化<a class="anchor-link" href="3-feature-extraction-and-preprocessing.html#%E6%95%B0%E6%8D%AE%E6%A0%87%E5%87%86%E5%8C%96">¶</a>
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<p>许多评估方法在处理标准化数据集时可以获得更好的效果。标准化数据均值为<code>0</code>，单位方差(Unit Variance)。均值为<code>0</code>的解释变量是关于原点对称的，特征向量的单位方差表示其特征值全身统一单位，统一量级的数据。例如，假设特征向量由两个解释变量构成，第一个变量值范围<code>[0,1]</code>，第二个变量值范围<code>[0,1000000]</code>，这时就要把第二个变量的值调整为<code>[0,1]</code>，这样才能保证数据是单位方差。如果变量特征值的量级比其他特征值的方差还大，这个特征值就会主导学习算法的方向，导致其他变量的影响被忽略。有些机器学习算法会在数据不标准时引入很小的优化参数值。解释变量的值可以通过正态分布进行标准化，减去均值后除以标准差。scikit-learn的<code>scale</code>函数可以实现：</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn</span> <span class="k">import</span> <span class="n">preprocessing</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span>
    <span class="p">[</span><span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">5.</span><span class="p">,</span> <span class="mf">13.</span><span class="p">,</span> <span class="mf">9.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span>
    <span class="p">[</span><span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">13.</span><span class="p">,</span> <span class="mf">15.</span><span class="p">,</span> <span class="mf">10.</span><span class="p">,</span> <span class="mf">15.</span><span class="p">],</span>
    <span class="p">[</span><span class="mf">0.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">,</span> <span class="mf">15.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="mf">11.</span><span class="p">]</span>
<span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="n">preprocessing</span><span class="o">.</span><span class="n">scale</span><span class="p">(</span><span class="n">X</span><span class="p">))</span>
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<pre>[[ 0.         -0.70710678 -1.38873015  0.52489066  0.59299945 -1.35873244]
 [ 0.         -0.70710678  0.46291005  0.87481777  0.81537425  1.01904933]
 [ 0.          1.41421356  0.9258201  -1.39970842 -1.4083737   0.33968311]]
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<h3 id="总结">总结<a class="anchor-link" href="3-feature-extraction-and-preprocessing.html#%E6%80%BB%E7%BB%93">¶</a>
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<p>本章我们介绍了特征提取的方法的基础知识，将不同类型的数据转换成特征向量方便机器学习算法研究。首先，我们介绍了分类数据的独热编码方法，并用scikit-learn的<code>DictVectorizer</code>类实现。然后，介绍了许多机器学习问题中常见的文档特征向量。通过词库模型将文档转换成词块的频率构成的特征向量，用<code>CountVectorizer</code>类计算基本单词频次的二进制特征向量。最后，通过停用词过滤，词根还原，词形还原进一步优化特征向量，并加入TF-IDF权重调整文集常见词，消除文档长度对特征向量的影响。</p>
<p>之后，我们介绍了图像特征提取的方法。首先，我们介绍了一个关于的手写数字识别的OCR问题，通过图像的像素矩阵扁平化来学习手写数字特征。这种方法非常耗费资源，于是我们引入兴趣点提取方法，通过SIFT和SURF进行优化。</p>
<p>最后介绍了数据标准化的方法，确保解释变量的数据都是同一量级，均值为<code>0</code>的标准化数据。特征提取技术在后面的章节中会不断使用。下一章，我们把词库模型和多元线性回归方法结合来实现文档分类。</p>

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